EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

Identifying Water Network Anomalies Using Multi Parameters Random Walk: Theory and Practice

A noise pattern analysis is used to demonstrate how water quality events can be classified. The algorithm presented mimics a random walk process in order to measure the level and type of noise in the water quality data. The resulting curve is analyzed and four different cases are identified. i.e. sensor problem, water source change, operational change and contamination. For each problem, the algorithm identifies a different pattern. This pattern can be used later to reduce the level of false alarms in the monitoring system

Outlier detection in dental research

In clinical dental research, errors occur in spite of careful study design and conduct. Data cleaning procedures intend to identify and correct these errors or at least to minimize their influence on study. Outlier is the one of these errors. Outlier detection is the first step in data analysis process which has a serious effect in the field of dental research. Hence, this paper aims to introduce the methods to detect the outliers and to examine their influences in statistical data analysis.ope

Comparación de la efectividad de procedimientos de la explotación de información para la identificación de outliers en bases de datos

La auditoría de sistemas tiene una función central en la prevención de riesgos relacionados con la tecnología de la información. En general se observa un escaso desarrollo de las técnicas de auditoría asistidas por computadora (TAACs). La Minería de Datos (MD) se aplica en forma incipiente y poco sistemática a tareas relacionadas con la auditoría de sistemas. El presente trabajo desarrolla el estado del arte en lo relacionado a las aplicaciones de la MD vinculada a la detección de datos anómalos, el desarrollo de procedimientos que permiten detectar campos anómalos en bases de datos y la experimentación de los procedimientos diseñados que permiten comprobar la eficacia de los mismos.Eje: Base de datos y minería de datosRed de Universidades con Carreras en Informática (RedUNCI